{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "data_train = pd.read_csv('engineered_data_train.csv')\n",
    "data_test = pd.read_csv('engineered_data_test.csv')\n",
    "\n",
    "X_train = data_train.drop('Y', axis=1)\n",
    "y_train = data_train['Y']\n",
    "\n",
    "X_test = data_test.drop('Y', axis=1)\n",
    "y_test = data_test['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4654171925730117"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.sum()/len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "def normalize(df):\n",
    "    data = df.copy()\n",
    "    feature_num = data.shape[1] - 1\n",
    "    \n",
    "    # recale features\n",
    "    normalized_values = preprocessing.scale(data.iloc[:, :feature_num].values)\n",
    "    for i in range(feature_num):\n",
    "        data.iloc[:, i] = normalized_values[:, i]\n",
    "    \n",
    "    data_clean = data\n",
    "    return data_clean\n",
    "\n",
    "# scale data\n",
    "X_train_scaled = normalize(X_train)\n",
    "X_test_scaled = normalize(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression (L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.6763662171753017\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression(penalty='l2', C=1e30)\n",
    "lr.fit(X_train.values, y_train.values)\n",
    "\n",
    "preds_lr = lr.predict_proba(X_test.values)[:, 1]\n",
    "fpr_lr, tpr_lr, _ = roc_curve(y_test.values, preds_lr)\n",
    "auc_lr = auc(fpr_lr, tpr_lr)\n",
    "\n",
    "accuracy_lr = accuracy_score(y_test, lr.predict(X_test))\n",
    "print('Accuracy: ', accuracy_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12bd1e588>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC\n",
    "fig = plt.figure(figsize = (8, 5))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "plt.plot(fpr_lr, tpr_lr, label='Logistic Regression: ' + '(AUC = %0.3f)'%auc_lr)\n",
    "plt.plot([0,1], [0,1], 'k--')\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('FPR',fontsize=15)\n",
    "plt.ylabel('TPR',fontsize=15)\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression (L2) with Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=1000, multi_class='warn',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='lbfgs',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'C': [1e-08, 1e-07, 1e-06, 1e-05, 0.0001, 0.001, 0.01,\n",
       "                               0.1, 1.0, 10.0, 100.0]},\n",
       "             pre_dispatch='2*n_jobs', refit=False, return_train_score=False,\n",
       "             scoring=['accuracy', 'roc_auc', 'f1', 'recall'], verbose=0)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameters = {'C': [10**n for n in np.arange(-8, 3, dtype=float)]}\n",
    "cs = [10**n for n in np.arange(-8, 3, dtype=float)]\n",
    "parameters = {'C': cs}\n",
    "\n",
    "# scores\n",
    "# def tn(y_true, y_pred): return confusion_matrix(y_true, y_pred)[0, 0]\n",
    "# def fp(y_true, y_pred): return confusion_matrix(y_true, y_pred)[0, 1]\n",
    "# def fn(y_true, y_pred): return confusion_matrix(y_true, y_pred)[1, 0]\n",
    "# def tp(y_true, y_pred): return confusion_matrix(y_true, y_pred)[1, 1]\n",
    "scores = ['accuracy', 'roc_auc', 'f1', 'recall', ]\n",
    "\n",
    "lr = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
    "lr_grid = GridSearchCV(lr, parameters, cv=5, scoring=scores, refit=False, verbose=1)\n",
    "lr_grid.fit(X_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time', 'param_C', 'params', 'split0_test_accuracy', 'split1_test_accuracy', 'split2_test_accuracy', 'split3_test_accuracy', 'split4_test_accuracy', 'mean_test_accuracy', 'std_test_accuracy', 'rank_test_accuracy', 'split0_test_roc_auc', 'split1_test_roc_auc', 'split2_test_roc_auc', 'split3_test_roc_auc', 'split4_test_roc_auc', 'mean_test_roc_auc', 'std_test_roc_auc', 'rank_test_roc_auc', 'split0_test_f1', 'split1_test_f1', 'split2_test_f1', 'split3_test_f1', 'split4_test_f1', 'mean_test_f1', 'std_test_f1', 'rank_test_f1', 'split0_test_recall', 'split1_test_recall', 'split2_test_recall', 'split3_test_recall', 'split4_test_recall', 'mean_test_recall', 'std_test_recall', 'rank_test_recall'])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_grid.cv_results_.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Best c is:  3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Log10(c)')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "cs_mean = lr_grid.cv_results_['mean_test_roc_auc']\n",
    "cs_std = lr_grid.cv_results_['std_test_roc_auc']\n",
    "cs_ste = cs_std / np.sqrt(len(cs))\n",
    "c_max_mean = np.where(cs_mean==cs_mean.max())[0][0]\n",
    "print('The Best c is: ', c_max_mean)\n",
    "max_1std = cs_mean[c_max_mean] - cs_ste[c_max_mean]\n",
    "\n",
    "fig = plt.figure(figsize = (15, 8))\n",
    "ax = plt.subplot(111)\n",
    "plt.plot(np.log10(cs), cs_mean)\n",
    "plt.plot(np.log10(cs), cs_mean-2*cs_ste, 'k+')\n",
    "plt.plot(np.log10(cs), cs_mean+2*cs_ste, 'k--')\n",
    "plt.plot([np.log10(cs)[0], np.log10(cs)[-1]], [max_1std, max_1std], 'r')\n",
    "\n",
    "# plt.ylim([0.5, 0.9])\n",
    "plt.xticks(np.log10(cs))\n",
    "plt.xlabel('Log10(c)',fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### retrain a logistic regression with C=1e-5 with all training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7015170333569908\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(penalty='l2', C=1e-5, solver='lbfgs', max_iter=1000)\n",
    "lr.fit(X_train.values, y_train.values)\n",
    "\n",
    "preds_lr = lr.predict_proba(X_test.values)[:, 1]\n",
    "fpr_lr, tpr_lr, _ = roc_curve(y_test.values, preds_lr)\n",
    "auc_lr = auc(fpr_lr, tpr_lr)\n",
    "\n",
    "accuracy_lr = accuracy_score(y_test, lr.predict(X_test))\n",
    "print('Accuracy: ', accuracy_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9516068907422346"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train auc\n",
    "preds_lr = lr.predict_proba(X_train.values)[:, 1]\n",
    "fpr_lr, tpr_lr, _ = roc_curve(y_train.values, preds_lr)\n",
    "auc_lr = auc(fpr_lr, tpr_lr)\n",
    "auc_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12ce7c198>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC\n",
    "fig = plt.figure(figsize = (8, 5))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "plt.plot(fpr_lr, tpr_lr, label='Logistic Regression: ' + '(AUC = %0.3f)'%auc_lr)\n",
    "plt.plot([0,1], [0,1], 'k--')\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('FPR',fontsize=15)\n",
    "plt.ylabel('TPR',fontsize=15)\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic regression is no good -> the relationship is not linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM (Super SLOW! Ignore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]"
     ]
    }
   ],
   "source": [
    "# from sklearn.svm import SVC\n",
    "\n",
    "# svm = SVC(kernel=\"linear\", probability=True, verbose=True)\n",
    "# svm.fit(X_train_scaled.values, y_train.values)\n",
    "\n",
    "# preds_svm = svm.predict_proba(X_test_scaled.values)[:, 1]\n",
    "\n",
    "# fpr_svm, tpr_svm, _ = roc_curve(y_test.values, preds_svm)\n",
    "# auc_svm = auc(fpr_svm, tpr_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree (without Cross-Validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7779897090134847\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy')\n",
    "tree.fit(X_train.values, y_train.values)\n",
    "\n",
    "preds_tree = tree.predict_proba(X_test.values)[:, 1]\n",
    "\n",
    "fpr_tree, tpr_tree, _ = roc_curve(y_test.values, preds_tree)\n",
    "auc_tree = auc(fpr_tree, tpr_tree)\n",
    "\n",
    "accuracy_tree = accuracy_score(y_test, tree.predict(X_test))\n",
    "print('Accuracy: ', accuracy_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.get_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12c4d1438>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC\n",
    "fig = plt.figure(figsize = (8, 5))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "plt.plot(fpr_tree, tpr_tree, label='Logistic Regression: ' + '(AUC = %0.3f)'%auc_tree)\n",
    "plt.plot([0,1], [0,1], 'k--')\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('FPR',fontsize=15)\n",
    "plt.ylabel('TPR',fontsize=15)\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree (with Cross-Validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 240 candidates, totalling 1200 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 1200 out of 1200 | elapsed:  9.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                              criterion='entropy',\n",
       "                                              max_depth=None, max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort=False, random_state=None,\n",
       "                                              splitter='best'),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'max_depth': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n",
       "                                       16, 17, 18, 19, 20],\n",
       "                         'min_samples_split': [2, 12, 23, 34, 45, 56, 67, 78,\n",
       "                                               89, 100, 120, 190, 260, 330,\n",
       "                                               400]},\n",
       "             pre_dispatch='2*n_jobs', refit=False, return_train_score=False,\n",
       "             scoring=['accuracy', 'roc_auc', 'f1', 'recall'], verbose=1)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting 5 folds for each of 25200 candidates, totalling 126000 fits\n",
    "# parameters = {\n",
    "#     'criterion': ['gini', 'entropy'],\n",
    "#     'max_depth': [i for i in range(5, 26)],\n",
    "#     'min_samples_split': np.arange(start=0.00033, stop=0.01, step=0.00033).tolist(),\n",
    "#     'min_samples_leaf': np.linspace(1, 150, 20, dtype='int').tolist()\n",
    "# }\n",
    "\n",
    "parameters = {\n",
    "    'max_depth': [i for i in range(5, 21)],\n",
    "    'min_samples_split': np.linspace(2, 100, 10, dtype='int').tolist() + np.linspace(120, 400, 5, dtype='int').tolist(),\n",
    "}\n",
    "\n",
    "scores = ['accuracy', 'roc_auc', 'f1', 'recall', ]\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy')\n",
    "tree_grid = GridSearchCV(tree, parameters, cv=5, scoring=scores, refit=False, verbose=1)\n",
    "tree_grid.fit(X_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time', 'param_max_depth', 'param_min_samples_split', 'params', 'split0_test_accuracy', 'split1_test_accuracy', 'split2_test_accuracy', 'split3_test_accuracy', 'split4_test_accuracy', 'mean_test_accuracy', 'std_test_accuracy', 'rank_test_accuracy', 'split0_test_roc_auc', 'split1_test_roc_auc', 'split2_test_roc_auc', 'split3_test_roc_auc', 'split4_test_roc_auc', 'mean_test_roc_auc', 'std_test_roc_auc', 'rank_test_roc_auc', 'split0_test_f1', 'split1_test_f1', 'split2_test_f1', 'split3_test_f1', 'split4_test_f1', 'mean_test_f1', 'std_test_f1', 'rank_test_f1', 'split0_test_recall', 'split1_test_recall', 'split2_test_recall', 'split3_test_recall', 'split4_test_recall', 'mean_test_recall', 'std_test_recall', 'rank_test_recall'])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_grid.cv_results_.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Best parameters is:  {'max_depth': 10, 'min_samples_split': 260}\n",
      "\n",
      "Combinations that higher than max 1std:\n",
      "{'max_depth': 10, 'min_samples_split': 260}\n",
      "{'max_depth': 10, 'min_samples_split': 330}\n"
     ]
    }
   ],
   "source": [
    "# calculate max 1std\n",
    "num_models = len(tree_grid.cv_results_['mean_test_roc_auc'])\n",
    "mean = tree_grid.cv_results_['mean_test_roc_auc']\n",
    "std = tree_grid.cv_results_['std_test_roc_auc']\n",
    "ste = std / np.sqrt(num_models)\n",
    "max_mean_index = np.where(mean==mean.max())[0][0]\n",
    "print('The Best parameters is: ', tree_grid.cv_results_['params'][max_mean_index])\n",
    "max_1std = mean[max_mean_index] - ste[max_mean_index]\n",
    "print()\n",
    "\n",
    "\n",
    "# select best combination\n",
    "print('Combinations that higher than max 1std:')\n",
    "for i in range(num_models):\n",
    "    if mean[i] > max_1std:\n",
    "        print(tree_grid.cv_results_['params'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Best parameters is:  {'max_depth': 10, 'min_samples_split': 260}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Log10(c)')"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# max_depth_list = []\n",
    "# min_samples_split_list = []\n",
    "# for i in tree_grid.cv_results_['params']:\n",
    "#     max_depth_list.append(i['max_depth'])\n",
    "#     min_samples_split_list.append(i['min_samples_split'])\n",
    "\n",
    "# plot\n",
    "mean = tree_grid.cv_results_['mean_test_roc_auc']\n",
    "std = tree_grid.cv_results_['std_test_roc_auc']\n",
    "ste = std / np.sqrt(len(tree_grid.cv_results_['mean_test_roc_auc']))\n",
    "max_mean_index = np.where(mean==mean.max())[0][0]\n",
    "print('The Best parameters is: ', tree_grid.cv_results_['params'][max_mean_index])\n",
    "max_1std = mean[max_mean_index] - ste[max_mean_index]\n",
    "\n",
    "fig = plt.figure(figsize = (15, 8))\n",
    "ax = plt.subplot(111)\n",
    "x = [i for i in range(len(tree_grid.cv_results_['mean_test_roc_auc']))]\n",
    "plt.plot(x, mean)\n",
    "plt.plot(x, mean-2*ste, 'k+')\n",
    "plt.plot(x, mean+2*ste, 'k--')\n",
    "plt.plot([x[0], x[-1]], [max_1std, max_1std], 'r')\n",
    "\n",
    "# plt.ylim([0.5, 0.9])\n",
    "plt.xticks(np.log10(cs))\n",
    "plt.xlabel('Log10(c)',fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot min_samples_split\n",
    "cs_mean = lr_grid.cv_results_['mean_test_roc_auc']\n",
    "cs_std = lr_grid.cv_results_['std_test_roc_auc']\n",
    "cs_ste = cs_std / np.sqrt(len(cs))\n",
    "c_max_mean = np.where(cs_mean==cs_mean.max())[0][0]\n",
    "print('The Best c is: ', c_max_mean)\n",
    "max_1std = cs_mean[c_max_mean] - cs_ste[c_max_mean]\n",
    "\n",
    "fig = plt.figure(figsize = (15, 8))\n",
    "ax = plt.subplot(111)\n",
    "plt.plot(np.log10(cs), cs_mean)\n",
    "plt.plot(np.log10(cs), cs_mean-2*cs_ste, 'k+')\n",
    "plt.plot(np.log10(cs), cs_mean+2*cs_ste, 'k--')\n",
    "plt.plot([np.log10(cs)[0], np.log10(cs)[-1]], [max_1std, max_1std], 'r')\n",
    "\n",
    "# plt.ylim([0.5, 0.9])\n",
    "plt.xticks(np.log10(cs))\n",
    "plt.xlabel('Log10(c)',fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7672551454932577\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "\n",
    "nb = BernoulliNB()\n",
    "nb.fit(X_train.values, y_train.values)\n",
    "\n",
    "preds_nb = nb.predict_proba(X_test.values)[:, 1]\n",
    "\n",
    "fpr_nb, tpr_nb, _ = roc_curve(y_test.values, preds_nb)\n",
    "auc_nb = auc(fpr_nb, tpr_nb)\n",
    "\n",
    "accuracy_nb = accuracy_score(y_test, nb.predict(X_test))\n",
    "print('Accuracy: ', accuracy_nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12c55fe48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC\n",
    "fig = plt.figure(figsize = (8, 5))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "plt.plot(fpr_nb, tpr_nb, label='Logistic Regression: ' + '(AUC = %0.3f)'%auc_nb)\n",
    "plt.plot([0,1], [0,1], 'k--')\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('FPR',fontsize=15)\n",
    "plt.ylabel('TPR',fontsize=15)\n",
    "plt.legend(fontsize=15)"
   ]
  }
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